{
 "metadata": {
  "name": "",
  "signature": "sha256:6143f878549a2d6a8b5e819bcec95525314ec3036ecc128f786c269925d4854d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import scipy.integrate\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from numpy import f2py\n",
      "import copy\n",
      "%matplotlib inline\n",
      "\n",
      "# local modules\n",
      "from periodic import PeriodicProcess, PeriodicScheduler, \\\n",
      "    nested_dict_to_namedtuple, Logger\n",
      "import uorb\n",
      "from sympy_utils import rhs_to_scipy_ode, \\\n",
      "    save_sympy_expr, load_sympy_expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "if f2py.compile(source=open('pendulum.f90','r').read(), modulename='pendulum',\n",
      "             source_fn='pendulum.f90'):\n",
      "    raise RuntimeError('compile failed, see console')\n",
      "import pendulum"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class PendulumDynamics(PeriodicProcess):\n",
      "\n",
      "    def __init__(self, period, x0, uorb_manager):\n",
      "        super(PendulumDynamics, self).__init__(period)\n",
      "        self.x0 = x0\n",
      "        self.ode = scipy.integrate.ode(\n",
      "            pendulum.compute_f, pendulum.compute_a)\n",
      "        #self.ode.set_integrator('dopri5')\n",
      "        self.uorb_manager = uorb_manager\n",
      "        self.process_noise_power = 1e-5\n",
      "\n",
      "    def initialize(self, t):\n",
      "        super(PendulumDynamics, self).initialize(t)\n",
      "        self.ode.set_initial_value(self.x0, t)\n",
      "        \n",
      "        # publish sim state\n",
      "        self.sim = uorb.Publication(\n",
      "            self.uorb_manager, 'sim_state',\n",
      "            uorb.Topic_sim_state(*((0,)*16)))\n",
      "        self.sim.publish()\n",
      "        \n",
      "        # subscribe to actuators\n",
      "        self.actuators = uorb.Subscription(\n",
      "            self.uorb_manager, 'actuator_outputs')\n",
      "        \n",
      "    def run(self, t):\n",
      "        # update actuator info\n",
      "        self.actuators.update()\n",
      "        \n",
      "        process_noise_stdev = np.sqrt(self.process_noise_power/self.period)\n",
      "        process_noise = process_noise_stdev*np.random.randn()\n",
      "        \n",
      "        ode = self.ode\n",
      "        if t == ode.t:\n",
      "            return\n",
      "        u = [self.actuators.data.output[0] + process_noise]\n",
      "        m = 1\n",
      "        g = 9.8\n",
      "        l = 1\n",
      "        ode.set_f_params(u, m, g, l)\n",
      "        ode.set_jac_params(u, m, g, l)\n",
      "        ode.integrate(t)\n",
      "        if not ode.successful():\n",
      "            raise ValueError('ode integration failed')\n",
      "            \n",
      "        # acceleration\n",
      "        # TODO, should make sympy output this\n",
      "        x = ode.y\n",
      "        y_accel = pendulum.compute_g(t, x, u, m, g, l)\n",
      "\n",
      "        # publish simulation data\n",
      "        self.sim.data.timestamp=int(1e6*t)\n",
      "        self.sim.data.pitch=ode.y[0]\n",
      "        self.sim.data.pitchspeed=ode.y[1]\n",
      "        self.sim.data.xacc=y_accel[0,0]\n",
      "        self.sim.data.yacc=y_accel[1,0]\n",
      "        self.sim.data.zacc=y_accel[2,0]\n",
      "        self.sim.publish()\n",
      "\n",
      "    @property\n",
      "    def x(self):\n",
      "        return self.ode.y"
     ],
     "language": "python",
     "metadata": {
      "code_folding": []
     },
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class Sensor(PeriodicProcess):\n",
      "    \n",
      "    def __init__(self, period, uorb_manager):\n",
      "        super(Sensor, self).__init__(period)\n",
      "        self.uorb_manager = uorb_manager\n",
      "\n",
      "        self.gyro_noise_power = 1e-5**np.ones(3)\n",
      "        self.gyro_bias = np.zeros(3)\n",
      "        self.accel_noise_power = 1e-4*np.ones(3)\n",
      "        self.mag_noise_power = 1e-4*np.ones(3)\n",
      "        \n",
      "    def initialize(self, t):\n",
      "        super(Sensor, self).initialize(t)     \n",
      "        \n",
      "        #initialize topic\n",
      "        timestamp = int(t*1e6)\n",
      "        self.sensor = uorb.Publication(\n",
      "            self.uorb_manager, 'sensor_combined',\n",
      "            uorb.Topic_sensor_combined(*((0,)*42)))\n",
      "        \n",
      "        self.sensor.data.gyro1_rad_s = timestamp\n",
      "        self.sensor.data.gyro_rad_s = np.array([0,0,0])\n",
      "        self.sensor.data.accelerometer_timestamp = timestamp\n",
      "        self.sensor.data.accelerometer_m_s2 = np.array([0,0,0])\n",
      "        self.sensor.magnetometer_timestamp = timestamp\n",
      "        self.sensor.magnetometer_ga = np.array([0,0,0])\n",
      "        self.sensor.publish()\n",
      "        \n",
      "        # get sim data\n",
      "        self.sim = uorb.Subscription(\n",
      "            self.uorb_manager, 'sim_state')\n",
      "\n",
      "    def run(self, t):\n",
      "        # get new simulation data\n",
      "        self.sim.update()\n",
      "        \n",
      "        # noise generator\n",
      "        randn = np.random.randn\n",
      "\n",
      "        timestamp = int(t*1e6)\n",
      "        self.sensor.data.timestamp = timestamp\n",
      "        \n",
      "        # gyro\n",
      "        gyro_ideal = np.array([self.sim.data.rollspeed,\n",
      "                     self.sim.data.pitchspeed,\n",
      "                     self.sim.data.yawspeed])\n",
      "        gyro_stddev = np.sqrt(self.gyro_noise_power/self.period)\n",
      "        gyro_noise = gyro_stddev*randn(3)\n",
      "        gyro = gyro_ideal + gyro_noise\n",
      "        \n",
      "        # accelerometer\n",
      "        accel_ideal = np.array([self.sim.data.xacc,self.sim.data.yacc,self.sim.data.zacc])\n",
      "        accel_stddev = np.sqrt(self.accel_noise_power/self.period)\n",
      "        accel_noise = accel_stddev*randn(3)\n",
      "        accel = accel_ideal + accel_noise\n",
      "\n",
      "        # magnetometer\n",
      "        mag_ideal = np.array([0,0,0])\n",
      "        mag_stddev = np.sqrt(self.mag_noise_power/self.period)\n",
      "        mag_noise = mag_stddev*randn(3)\n",
      "        mag = mag_ideal + mag_noise\n",
      "        \n",
      "        # publish\n",
      "        self.sensor.data.gyro1_timestamp = timestamp\n",
      "        self.sensor.data.gyro_rad_s = gyro\n",
      "        self.sensor.data.accelerometer_timestamp = timestamp\n",
      "        self.sensor.data.accelerometer_m_s2 = accel\n",
      "        self.sensor.magnetometer_timestamp = timestamp\n",
      "        self.sensor.magnetometer_ga = mag\n",
      "        self.sensor.publish()"
     ],
     "language": "python",
     "metadata": {
      "code_folding": []
     },
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class Estimator(PeriodicProcess):\n",
      "\n",
      "    def __init__(self, period, uorb_manager):\n",
      "        super(Estimator, self).__init__(period)\n",
      "        self.uorb_manager = uorb_manager\n",
      "        self.xh = np.array([0,0])\n",
      "        self.P0 = np.eye(2)\n",
      "\n",
      "    def initialize(self, t):\n",
      "        super(Estimator, self).initialize(t)\n",
      "        # initialize position publication\n",
      "        self.pos = uorb.Publication(\n",
      "            self.uorb_manager,\n",
      "            'vehicle_global_position',\n",
      "            uorb.Topic_vehicle_global_position(*((0,)*11)))\n",
      "        \n",
      "        # initialize attitude publication\n",
      "        self.att = uorb.Publication(\n",
      "            self.uorb_manager,\n",
      "            'vehicle_attitude',\n",
      "            uorb.Topic_vehicle_attitude(*((0,)*16)))\n",
      "        \n",
      "        # intialize sensor subscription\n",
      "        self.sensor = uorb.Subscription(\n",
      "            self.uorb_manager,\n",
      "            'sensor_combined')\n",
      "\n",
      "        # TODO, this is a hack, we make the estimator\n",
      "        # just give the sim state for now\n",
      "        self.sim = uorb.Subscription(\n",
      "            self.uorb_manager,\n",
      "            'sim_state')\n",
      "        \n",
      "        # initialize timestamps for checking for sensor updates\n",
      "        self.accel_timestamp_last = -1\n",
      "        self.l = 1\n",
      "        self.g = 9.8\n",
      "        \n",
      "    def run(self, t):\n",
      "        # get new sensor data\n",
      "        # TODO use this to estimate\n",
      "        self.sensor.update()\n",
      "        \n",
      "        if self.sensor.data.accelerometer_timestamp != self.accel_timestamp_last:\n",
      "            self.accel_timestamp_last = self.sensor.data.accelerometer_timestamp\n",
      "            y = self.sensor.data.accelerometer_m_s2\n",
      "            \n",
      "            theta = self.xh[0]\n",
      "            theta_dot = self.xh[1]\n",
      "            theta_ddot = 1 # TODO\n",
      "            yh = np.array([self.l*theta_ddot + self.g*np.sin(theta), 0, self.g*np.cos(theta)])\n",
      "            yh = []\n",
      "            \n",
      "        # publish new estimated position\n",
      "        # TODO don't cheat using sim state\n",
      "        self.pos.data.timestamp = int(1e6*t)\n",
      "        self.pos.data.lat = self.sim.data.lat\n",
      "        self.pos.data.lon = self.sim.data.lon\n",
      "        self.pos.data.alt = self.sim.data.alt\n",
      "        self.pos.data.vel_n = self.sim.data.vx\n",
      "        self.pos.data.vel_e = self.sim.data.vy\n",
      "        self.pos.data.vel_d = self.sim.data.vz\n",
      "        self.pos.data.yaw = self.sim.data.yaw\n",
      "        self.pos.publish()\n",
      "        \n",
      "        # publish new estimated attitude\n",
      "        self.att.data.timestamp = int(1e6*t)\n",
      "        self.att.data.roll = self.sim.data.roll\n",
      "        self.att.data.pitch = float(self.sim.data.pitch)\n",
      "        self.att.data.yaw = self.sim.data.yaw\n",
      "        self.att.data.rollspeed = self.sensor.data.gyro_rad_s[0]\n",
      "        self.att.data.pitchspeed = self.sensor.data.gyro_rad_s[1]\n",
      "        self.att.data.yawspeed = self.sensor.data.gyro_rad_s[2]\n",
      "        self.att.publish()"
     ],
     "language": "python",
     "metadata": {
      "code_folding": []
     },
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class Controller(PeriodicProcess):\n",
      "\n",
      "    def __init__(self, period, uorb_manager):\n",
      "        super(Controller, self).__init__(period)\n",
      "        self.uorb_manager = uorb_manager\n",
      "        self.actuators = uorb.Publication(\n",
      "            self.uorb_manager, 'actuator_outputs',\n",
      "            uorb.Topic_actuator_outputs(0,\n",
      "                output=np.array([0,0,0,0,0,0,0,0]).astype(float),\n",
      "                noutputs=1))\n",
      "        self.actuators.publish()\n",
      "        \n",
      "    def initialize(self, t):\n",
      "        super(Controller, self).initialize(t)\n",
      "        \n",
      "        # publish actuator topic\n",
      "        self.actuators.data = uorb.Topic_actuator_outputs(int(t*1e6),\n",
      "                output=np.array([0,0,0,0,0,0,0,0]).astype(float))\n",
      "        self.actuators.publish()\n",
      "        \n",
      "        # subscribe to vehicle global position topic\n",
      "        self.pos = uorb.Subscription(\n",
      "            self.uorb_manager, 'vehicle_global_position')\n",
      "        \n",
      "        self.att = uorb.Subscription(\n",
      "            self.uorb_manager, 'vehicle_attitude')\n",
      "        \n",
      "    def run(self, t):\n",
      "        # get new position/attitude estimation\n",
      "        self.pos.update()\n",
      "        self.att.update()\n",
      "        \n",
      "        u = -20*self.att.data.pitch - 6*self.att.data.pitchspeed\n",
      "        \n",
      "        # publish new actuator controls\n",
      "        self.actuators.data.timestamp = int(t*1e6)\n",
      "        self.actuators.data.output = [u,0,0,0,0,0,0,0]\n",
      "        self.actuators.publish()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First we declare the uORB manager and all processes that will be running. Next, we create the scheduler and pass the list or processes to it. Finally, we run the scheudler."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def run_sim():\n",
      "    # constants\n",
      "    tf = 4\n",
      "\n",
      "    # declare uorb manager, by using pointer copying we can avoid\n",
      "    # copying actual data and make it run much faster, however \n",
      "    # we have to becareful doing this with multi-threading\n",
      "    uorb_manager = uorb.Manager(copy_type='pointer')\n",
      "\n",
      "    # declare periodic processes\n",
      "    controller = Controller(0.02, uorb_manager)\n",
      "    estimator = Estimator(0.002, uorb_manager)\n",
      "    dynamics = PendulumDynamics(0.001, [1,0], uorb_manager)\n",
      "    sensor = Sensor(0.001, uorb_manager)\n",
      "    logger = Logger(0.001, tf, \n",
      "        ['sim_state',\n",
      "         'vehicle_global_position',\n",
      "         'vehicle_attitude',\n",
      "         'sensor_combined',\n",
      "         'actuator_outputs'],\n",
      "        uorb_manager)\n",
      "\n",
      "    # declare scheduler\n",
      "    scheduler = PeriodicScheduler(False, 0, tf, 0.001)\n",
      "    scheduler.process_list = [\n",
      "        dynamics, sensor, estimator, controller, logger]  \n",
      "\n",
      "    # run the scheduler\n",
      "    scheduler.run()\n",
      "    return nested_dict_to_namedtuple(logger.log)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can profile the simulation to see what is taking the most time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats = %prun -q -r log = run_sim()\n",
      "stats.sort_stats('tottime').print_stats(20);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "          302502 function calls in 0.608 seconds\n",
        "\n",
        "   Ordered by: internal time\n",
        "   List reduced from 134 to 20 due to restriction <20>\n",
        "\n",
        "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
        "     3999    0.123    0.000    0.125    0.000 _ode.py:730(run)\n",
        "     3999    0.090    0.000    0.162    0.000 <ipython-input-4-5d4f043927be>:33(run)\n",
        "     3999    0.086    0.000    0.129    0.000 __init__.py:111(run)\n",
        "     3999    0.051    0.000    0.209    0.000 <ipython-input-3-ea772f26b4d7>:26(run)\n",
        "    13926    0.047    0.000    0.047    0.000 {numpy.core.multiarray.array}\n",
        "    15996    0.031    0.000    0.031    0.000 {method 'randn' of 'mtrand.RandomState' objects}\n",
        "     1833    0.027    0.000    0.044    0.000 <ipython-input-5-6fb25091c0a5>:39(run)\n",
        "    11865    0.021    0.000    0.027    0.000 _base.py:57(publish)\n",
        "    30205    0.018    0.000    0.056    0.000 _base.py:100(update)\n",
        "    20000    0.018    0.000    0.565    0.000 __init__.py:35(update)\n",
        "    30205    0.013    0.000    0.022    0.000 _base.py:97(updated)\n",
        "    18272    0.012    0.000    0.016    0.000 _base.py:83(copy)\n",
        "        1    0.010    0.010    0.603    0.603 __init__.py:59(run)\n",
        "    30205    0.010    0.000    0.010    0.000 _base.py:79(updated)\n",
        "    35872    0.009    0.000    0.009    0.000 {method 'keys' of 'dict' objects}\n",
        "    11865    0.007    0.000    0.034    0.000 _base.py:116(publish)\n",
        "    30137    0.007    0.000    0.007    0.000 _base.py:42(_copy_type)\n",
        "     3999    0.006    0.000    0.132    0.000 _ode.py:376(integrate)\n",
        "        1    0.004    0.004    0.025    0.025 __init__.py:123(finalize)\n",
        "        7    0.003    0.000    0.005    0.001 collections.py:288(namedtuple)\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we aren't profiling it is much faster."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "log = run_sim();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 513 ms, sys: 3.6 ms, total: 517 ms\n",
        "Wall time: 515 ms\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It is easy to plot any of the various uorb topics."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(log.log.t, log.sim_state.pitch);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x500a610>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(log.log.t, log.actuator_outputs.output[:,0], 'r');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x5806050>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(log.log.t, log.vehicle_attitude.pitch);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x53d8c90>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(log.log.t, log.sensor_combined.gyro_rad_s);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PH8YSgb0+D0iJYHnbzAjWuatxMsSvmAhCHBF3rp4APbcaE7eux8HhVogJfZjC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\n4/AVAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x582a150>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(log.log.t, log.sensor_combined.accelerometer_m_s2);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x5338250>"
       ]
      }
     ],
     "prompt_number": 14
    }
   ],
   "metadata": {}
  }
 ]
}